Self-Determining AI Agents is a Python-based framework designed to simplify the creation of autonomous AI agents. It features a customizable planning loop where agents generate tasks, plan strategies, and execute actions using integrated tools. The framework includes persistent memory modules for context retention, a flexible task scheduling system, and hooks for custom tool integrations such as web APIs or database queries. Developers define agent goals via configuration files or code, and the library handles the iterative decision-making process. It supports logging, performance monitoring, and can be extended with new planning algorithms. Ideal for research, automating workflows, and prototyping intelligent multi-agent systems.

Acme is a Python-based framework that simplifies the development and evaluation of reinforcement learning agents. It offers a collection of prebuilt agent implementations (e.g., DQN, PPO, SAC), environment wrappers, replay buffers, and distributed execution engines. Researchers can mix and match components to prototype new algorithms, monitor training metrics with built-in logging, and leverage scalable distributed pipelines for large-scale experiments. Acme integrates with TensorFlow and JAX, supports custom environments via OpenAI Gym interfaces, and includes utilities for checkpointing, evaluation, and hyperparameter configuration.